Deep Convolutional Neural Networks for Fire Detection in Images

  • Jivitesh Sharma
  • Ole-Christoffer Granmo
  • Morten Goodwin
  • Jahn Thomas Fidje
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 744)

Abstract

Detecting fire in images using image processing and computer vision techniques has gained a lot of attention from researchers during the past few years. Indeed, with sufficient accuracy, such systems may outperform traditional fire detection equipment. One of the most promising techniques used in this area is Convolutional Neural Networks (CNNs). However, the previous research on fire detection with CNNs has only been evaluated on balanced datasets, which may give misleading information on real-world performance, where fire is a rare event. Actually, as demonstrated in this paper, it turns out that a traditional CNN performs relatively poorly when evaluated on the more realistically balanced benchmark dataset provided in this paper. We therefore propose to use even deeper Convolutional Neural Networks for fire detection in images, and enhancing these with fine tuning based on a fully connected layer. We use two pretrained state-of-the-art Deep CNNs, VGG16 and Resnet50, to develop our fire detection system. The Deep CNNs are tested on our imbalanced dataset, which we have assembled to replicate real world scenarios. It includes images that are particularly difficult to classify and that are deliberately unbalanced by including significantly more non-fire images than fire images. The dataset has been made available online. Our results show that adding fully connected layers for fine tuning indeed does increase accuracy, however, this also increases training time. Overall, we found that our deeper CNNs give good performance on a more challenging dataset, with Resnet50 slightly outperforming VGG16. These results may thus lead to more successful fire detection systems in practice.

Keywords

Fire detection Deep Convolutional Neural Networks VGG16 Resnet50 

References

  1. 1.
    Bradski, G.: Opencv. Dr. Dobb’s Journal of Software Tools (2000)Google Scholar
  2. 2.
    Chino, D.Y.T., Avalhais, L.P.S., Rodrigues Jr., J.F., Traina, A.J.M.: Bowfire: detection of fire in still images by integrating pixel color and texture analysis. CoRR, abs/1506.03495 (2015)Google Scholar
  3. 3.
    Chollet, F.: Keras (2015)Google Scholar
  4. 4.
    Zhao, J., Zhang, Z., Qu, C., Ke, Y., Zhang, D., Han, S., Chen, X.: Image based forest fire detection using dynamic characteristics with artificial neural networks. In: 2009 International Joint Conference on Artificial Intelligence, pp. 290–293, April 2009Google Scholar
  5. 5.
    Frizzi, S., Kaabi, R., Bouchouicha, M., Ginoux, J.M., Moreau, E., Fnaiech, F.: Convolutional neural network for video fire and smoke detection. In: IECON 2016–42nd Annual Conference of the IEEE Industrial Electronics Society, pp. 877–882, October 2016Google Scholar
  6. 6.
    Fukushima, K.: Neocognitron: a self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biol. Cybern. 36(4), 193–202 (1980)CrossRefMATHGoogle Scholar
  7. 7.
    He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2016Google Scholar
  8. 8.
    Horng, W.-B., Peng, J.-W.: Image-based fire detection using neural networks. In: JCIS (2006)Google Scholar
  9. 9.
    Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. CoRR, abs/1412.6980 (2014)Google Scholar
  10. 10.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Pereira, F., Burges, C.J.C., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25, pp. 1097–1105. Curran Associates Inc. (2012)Google Scholar
  11. 11.
    Lecun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)CrossRefGoogle Scholar
  12. 12.
    Tomas Polednik, Bc.: Detection of fire in images and video using cnn. Excel@FIT (2015)Google Scholar
  13. 13.
    Poobalan, K., Liew, S.C.: Fire detection algorithm using image processing techniques. In: 3rd International Conference on Artificial Intelligence and Computer Science (AICS2015), Ocotober 2015Google Scholar
  14. 14.
    Custer, R.B.R.: Fire detection: The state of the art. NBS Technical Note, US Department of Commerce (1974)Google Scholar
  15. 15.
    Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A.C., Fei-Fei, L.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vis. (IJCV) 115(3), 211–252 (2015)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Shao, J., Wang, G., Guo, W.: An image-based fire detection method using color analysis. In: 2012 International Conference on Computer Science and Information Processing (CSIP), pp. 1008–1011, August 2012Google Scholar
  17. 17.
    Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. CoRR, abs/1409.1556 (2014)Google Scholar
  18. 18.
    Tao, C., Zhang, J., Wang, P.: Smoke detection based on deep convolutional neural networks. In: 2016 International Conference on Industrial Informatics - Computing Technology, Intelligent Technology, Industrial Information Integration (ICIICII), pp. 150–153, December 2016Google Scholar
  19. 19.
    Toulouse, T., Rossi, L., Celik, T., Akhloufi, M.: Automatic fire pixel detection using image processing: a comparative analysis of rule-based and machine learning-based methods. Sig. Image Video Process. 10(4), 647–654 (2016)CrossRefGoogle Scholar
  20. 20.
    Treyin, B.U., Dedeoglu, Y., Gkbay, U., Enisetin, A.: Computer vision based method for real-time fire and flame detection. Pattern Recogn. Lett. 27(1), 49–58 (2006)CrossRefGoogle Scholar
  21. 21.
    Verstockt, S., Lambert, P., Van de Walle, R., Merci, B., Sette, B.: State of the art in vision-based fire and smoke dectection. In: Luck, H., Willms, I. (eds.) International Conference on Automatic Fire Detection, 14th, Proceedings, vol. 2, pp. 285–292. University of Duisburg-Essen. Department of Communication Systems (2009)Google Scholar
  22. 22.
    Vicente, J., Guillemant, P.: An image processing technique for automatically detecting forest fire. Int. J. Thermal Sci. 41(12), 1113–1120 (2002)CrossRefGoogle Scholar
  23. 23.
    Zhang, Q., Xu, J., Xu, L., Guo, H.: Deep convolutional neural networks for forest fire detection, February 2016Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Jivitesh Sharma
    • 1
  • Ole-Christoffer Granmo
    • 1
  • Morten Goodwin
    • 1
  • Jahn Thomas Fidje
    • 1
  1. 1.University of Agder (UiA)KristiansandNorway

Personalised recommendations